Representing Uncertainty of Hierarchical and Response Surface Models to Improve Design of Experiments
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The Navy uses families of models of varying detail and focus to analyze forces and operational concepts. The information gleaned from these model- supported studies helps shape what the future Navy will look like and how it will fight. The current practice in the higher-to-lower-fidelity sequence of modeling is to use point estimates of more focused higher-fidelity model outputs as the inputs for the broader lower-fidelity models. It is vitally important to understand how these lower-level model errors are propagated through the series of models and how decisions are affected as a result. This research is reviewing previous efforts related to propagating errors in hierarchical models, empirically exploring the impacts of multiple approaches, and providing recommendations on extending and applying the methods—which will include uncertainty analysis, design of experiments (DOE), and preferred metamodel forms.
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